Lab 05: First contact with dplyr and ggplot2
Cindy Lo
9/26/2018
Filestructure and Shell commands
cd labs
mkdir lab05
cd lab05
mkdir data report images
ls
vim README.md
cat README.md
cd data
curl -O https://raw.githubusercontent.com/ucb-stat133/stat133-fall-2018/master/data/nba2018-players.csv
ls
wc nba2018-players.csv
head nba2018-players.csv
tail nba2018-players.csvInstalling packages
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(readr)NBA Players Data
dat <- read.csv('nba2018-players.csv', stringsAsFactors = FALSE)
dat <- read_csv('nba2018-players.csv')## Parsed with column specification:
## cols(
## player = col_character(),
## team = col_character(),
## position = col_character(),
## height = col_integer(),
## weight = col_integer(),
## age = col_integer(),
## experience = col_integer(),
## college = col_character(),
## salary = col_double(),
## games = col_integer(),
## minutes = col_integer(),
## points = col_integer(),
## points3 = col_integer(),
## points2 = col_integer(),
## points1 = col_integer()
## )Filtering, slicing, and selecting
slice() allows you to select rows by position:
three_rows <- slice(dat, 1:3)
three_rows## # A tibble: 3 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Al Ho… BOS C 82 245 30 9 Univer… 2.65e7 68
## 2 Amir … BOS PF 81 240 29 11 <NA> 1.20e7 80
## 3 Avery… BOS SG 74 180 26 6 Univer… 8.27e6 55
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>filter() allows you to select rows by condition:
gt_85 <- filter(dat, height > 85)
gt_85## # A tibble: 5 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Edy T… CLE C 87 260 24 1 <NA> 5.14e3 1
## 2 Boban… DET C 87 290 28 1 <NA> 7.00e6 35
## 3 Krist… NYK PF 87 240 21 1 <NA> 4.32e6 66
## 4 Roy H… DEN C 86 270 30 8 George… 5.00e6 6
## 5 Alexi… NOP C 86 248 28 6 <NA> 4.60e6 39
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>select() allows you to select columns by name:
player_height <- select(dat, player, height)
player_height## # A tibble: 477 x 2
## player height
## <chr> <int>
## 1 Al Horford 82
## 2 Amir Johnson 81
## 3 Avery Bradley 74
## 4 Demetrius Jackson 73
## 5 Gerald Green 79
## 6 Isaiah Thomas 69
## 7 Jae Crowder 78
## 8 James Young 78
## 9 Jaylen Brown 79
## 10 Jonas Jerebko 82
## # ... with 467 more rowsUse slice() to subset the data by selecting the first 5 rows
five_rows <- slice(dat, 1:5)
five_rows## # A tibble: 5 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Al Ho… BOS C 82 245 30 9 Univer… 2.65e7 68
## 2 Amir … BOS PF 81 240 29 11 <NA> 1.20e7 80
## 3 Avery… BOS SG 74 180 26 6 Univer… 8.27e6 55
## 4 Demet… BOS PG 73 201 22 0 Univer… 1.45e6 5
## 5 Geral… BOS SF 79 205 31 9 <NA> 1.41e6 47
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>Use slice() to subset the data by selecting rows 10, 15, 20, …, 50
by_five <- slice(dat, 5*(2:10))
by_five## # A tibble: 9 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Jonas… BOS PF 82 231 29 6 <NA> 5.00e6 78
## 2 Tyler… BOS C 84 253 27 4 Univer… 8.00e6 51
## 3 Derri… CLE PF 80 240 25 5 Univer… 4.02e5 25
## 4 Jorda… CLE SG 78 185 25 1 Univer… 8.75e5 37
## 5 Larry… CLE C 83 235 28 5 Virgin… 2.08e5 5
## 6 Cory … TOR PG 75 193 25 5 Univer… 7.33e6 80
## 7 Jakob… TOR C 84 248 21 0 Univer… 2.70e6 54
## 8 P.J. … TOR SF 78 245 31 5 Univer… 5.30e6 24
## 9 Bradl… WAS SG 77 207 23 4 Univer… 2.21e7 77
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>Use slice() to subset the data by selecting the last 5 rows
last_five <- slice(dat, 472:477)
last_five## # A tibble: 6 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Leand… PHO SG 75 194 34 13 <NA> 4.00e6 67
## 2 Marqu… PHO PF 82 233 19 0 Univer… 2.94e6 82
## 3 Ronni… PHO PG 74 190 33 11 Nichol… 4.65e5 14
## 4 T.J. … PHO SF 80 230 23 2 North … 2.13e6 66
## 5 Tyler… PHO PG 70 150 21 0 Univer… 9.18e5 61
## 6 Tyson… PHO C 85 240 34 15 <NA> 1.24e7 47
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>Use filter() to subset those players with height less than 70 inches tall
small_70 <- filter(dat, height < 70)
small_70## # A tibble: 2 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Isaia… BOS PG 69 185 27 5 Univer… 6.59e6 76
## 2 Kay F… CLE PG 69 176 21 0 Oaklan… 5.43e5 42
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>Use filter() to subset rows of GSW
nba_gsw <- filter(dat, team == "GSW")
nba_gsw## # A tibble: 16 x 15
## player team position height weight age experience college salary
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl>
## 1 Ander… GSW C 82 273 34 12 <NA> 1.55e6
## 2 Andre… GSW SF 78 215 33 12 Univer… 1.11e7
## 3 Damia… GSW C 84 245 21 0 Vander… 1.17e6
## 4 David… GSW C 81 250 36 13 Xavier… 1.55e6
## 5 Draym… GSW PF 79 230 26 4 Michig… 1.53e7
## 6 Ian C… GSW SG 75 175 25 3 Belmon… 1.02e6
## 7 James… GSW PF 81 230 24 2 Univer… 9.80e5
## 8 JaVal… GSW C 84 270 29 8 Univer… 1.40e6
## 9 Kevin… GSW PF 81 240 28 9 Univer… 2.65e7
## 10 Kevon… GSW C 81 220 20 1 Univer… 1.18e6
## 11 Klay … GSW SG 79 215 26 5 Washin… 1.67e7
## 12 Matt … GSW SF 79 226 36 13 Univer… 3.83e5
## 13 Patri… GSW SG 79 185 21 0 Univer… 5.43e5
## 14 Shaun… GSW PG 79 192 31 11 <NA> 5.78e6
## 15 Steph… GSW PG 75 190 28 7 Davids… 1.21e7
## 16 Zaza … GSW C 83 270 32 13 <NA> 2.90e6
## # ... with 6 more variables: games <int>, minutes <int>, points <int>,
## # points3 <int>, points2 <int>, points1 <int>Use filter() to subset rows of GSW centers
nba_gsw_C <- filter(nba_gsw, position == "C")
nba_gsw_C## # A tibble: 6 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Ander… GSW C 82 273 34 12 <NA> 1.55e6 14
## 2 Damia… GSW C 84 245 21 0 Vander… 1.17e6 10
## 3 David… GSW C 81 250 36 13 Xavier… 1.55e6 68
## 4 JaVal… GSW C 84 270 29 8 Univer… 1.40e6 77
## 5 Kevon… GSW C 81 220 20 1 Univer… 1.18e6 53
## 6 Zaza … GSW C 83 270 32 13 <NA> 2.90e6 70
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>Use filter() and then select() to subset rows of Lakers and then display their names
nba_lal <- filter(dat, team == "LAL")
nba_lal_players <- select(nba_lal, player)
nba_lal_players## # A tibble: 14 x 1
## player
## <chr>
## 1 Brandon Ingram
## 2 Corey Brewer
## 3 D'Angelo Russell
## 4 David Nwaba
## 5 Ivica Zubac
## 6 Jordan Clarkson
## 7 Julius Randle
## 8 Luol Deng
## 9 Metta World Peace
## 10 Nick Young
## 11 Tarik Black
## 12 Thomas Robinson
## 13 Timofey Mozgov
## 14 Tyler EnnisUse filter() and then select() to display the name and salary of GSW point guards
nba_gsw_PG <- filter(nba_gsw, position == "PG")
player_salary <- select(nba_gsw_PG, player, salary)
player_salary## # A tibble: 2 x 2
## player salary
## <chr> <dbl>
## 1 Shaun Livingston 5782450
## 2 Stephen Curry 12112359Find how to select the name, age, and team of players with more than 10 years of experience, making 10 million dollars or less
nba_10yr10mill <- filter(dat, experience > 10 & salary <= 10000000)
nba_10yr10millshow <- select(nba_10yr10mill, player, age, team)
nba_10yr10millshow## # A tibble: 36 x 3
## player age team
## <chr> <int> <chr>
## 1 Andrew Bogut 32 CLE
## 2 Dahntay Jones 36 CLE
## 3 Deron Williams 32 CLE
## 4 James Jones 36 CLE
## 5 Kyle Korver 35 CLE
## 6 Richard Jefferson 36 CLE
## 7 Jose Calderon 35 ATL
## 8 Kris Humphries 31 ATL
## 9 Mike Dunleavy 36 ATL
## 10 Jason Terry 39 MIL
## # ... with 26 more rowsFind how to select the name, team, height, and weight of rookie players, 20 years old, displaying only the first five occurrences
rookie_20 <- filter(dat, experience <= 10 & age == 20)
rookienthw <- select(rookie_20, player, team, height, weight)
head(rookienthw, 5)## # A tibble: 5 x 4
## player team height weight
## <chr> <chr> <int> <int>
## 1 Jaylen Brown BOS 79 225
## 2 Rashad Vaughn MIL 78 202
## 3 Myles Turner IND 83 243
## 4 Justise Winslow MIA 79 225
## 5 Henry Ellenson DET 83 245Adding new variables: mutate()
gsw <- filter(dat, team == "GSW")
gsw <- select(gsw, player, height, weight)
gsw <- slice(gsw, c(4, 8, 10, 14, 15))
gsw## # A tibble: 5 x 3
## player height weight
## <chr> <int> <int>
## 1 David West 81 250
## 2 JaVale McGee 84 270
## 3 Kevon Looney 81 220
## 4 Shaun Livingston 79 192
## 5 Stephen Curry 75 190mutate(gsw, ht_wt = height/weight)## # A tibble: 5 x 4
## player height weight ht_wt
## <chr> <int> <int> <dbl>
## 1 David West 81 250 0.324
## 2 JaVale McGee 84 270 0.311
## 3 Kevon Looney 81 220 0.368
## 4 Shaun Livingston 79 192 0.411
## 5 Stephen Curry 75 190 0.395gsw2 <- mutate(gsw, ht_m = height * 0.0254, wt_kg = weight * 0.4536)
gsw2## # A tibble: 5 x 5
## player height weight ht_m wt_kg
## <chr> <int> <int> <dbl> <dbl>
## 1 David West 81 250 2.06 113.
## 2 JaVale McGee 84 270 2.13 122.
## 3 Kevon Looney 81 220 2.06 99.8
## 4 Shaun Livingston 79 192 2.01 87.1
## 5 Stephen Curry 75 190 1.90 86.2Reordering rows: arrange()
arrange(gsw, height)## # A tibble: 5 x 3
## player height weight
## <chr> <int> <int>
## 1 Stephen Curry 75 190
## 2 Shaun Livingston 79 192
## 3 David West 81 250
## 4 Kevon Looney 81 220
## 5 JaVale McGee 84 270arrange(gsw, desc(height))## # A tibble: 5 x 3
## player height weight
## <chr> <int> <int>
## 1 JaVale McGee 84 270
## 2 David West 81 250
## 3 Kevon Looney 81 220
## 4 Shaun Livingston 79 192
## 5 Stephen Curry 75 190arrange(gsw, height, weight)## # A tibble: 5 x 3
## player height weight
## <chr> <int> <int>
## 1 Stephen Curry 75 190
## 2 Shaun Livingston 79 192
## 3 Kevon Looney 81 220
## 4 David West 81 250
## 5 JaVale McGee 84 270Using the data frame gsw add a new variable product with the product of height and weight
gsw <- mutate(gsw, product = height*weight)
gsw## # A tibble: 5 x 4
## player height weight product
## <chr> <int> <int> <int>
## 1 David West 81 250 20250
## 2 JaVale McGee 84 270 22680
## 3 Kevon Looney 81 220 17820
## 4 Shaun Livingston 79 192 15168
## 5 Stephen Curry 75 190 14250Create a new dataa frame gsw3, by adding columns log_height and log_weight with the log transformations of height and weight
gsw3 <- mutate(gsw, log_height = log(height), log_weight = log(weight))
gsw3## # A tibble: 5 x 6
## player height weight product log_height log_weight
## <chr> <int> <int> <int> <dbl> <dbl>
## 1 David West 81 250 20250 4.39 5.52
## 2 JaVale McGee 84 270 22680 4.43 5.60
## 3 Kevon Looney 81 220 17820 4.39 5.39
## 4 Shaun Livingston 79 192 15168 4.37 5.26
## 5 Stephen Curry 75 190 14250 4.32 5.25Use the original data frame to filter() and arrange() those players with height less than 71 inchess tall, in increasing order
small_71 <- filter(dat, height < 71)
arrange(small_71, height)## # A tibble: 4 x 15
## player team position height weight age experience college salary games
## <chr> <chr> <chr> <int> <int> <int> <int> <chr> <dbl> <int>
## 1 Isaia… BOS PG 69 185 27 5 Univer… 6.59e6 76
## 2 Kay F… CLE PG 69 176 21 0 Oaklan… 5.43e5 42
## 3 Pierr… DAL PG 70 180 25 0 Baylor… 1.05e5 8
## 4 Tyler… PHO PG 70 150 21 0 Univer… 9.18e5 61
## # ... with 5 more variables: minutes <int>, points <int>, points3 <int>,
## # points2 <int>, points1 <int>Display the name, team, and salary of the top-5 highest paid players
top_paid <- arrange(dat, desc(salary))
top_paid <- select(top_paid, player, team, salary)
head(top_paid, 5)## # A tibble: 5 x 3
## player team salary
## <chr> <chr> <dbl>
## 1 LeBron James CLE 30963450
## 2 Al Horford BOS 26540100
## 3 DeMar DeRozan TOR 26540100
## 4 Kevin Durant GSW 26540100
## 5 James Harden HOU 26540100Dislpay the name, team, and points3, of the top 10 three-point players
top_points3 <- arrange(dat, desc(points3))
top_points3 <- select(top_points3, player, team, points3)
head(top_points3, 10)## # A tibble: 10 x 3
## player team points3
## <chr> <chr> <int>
## 1 Stephen Curry GSW 324
## 2 Klay Thompson GSW 268
## 3 James Harden HOU 262
## 4 Eric Gordon HOU 246
## 5 Isaiah Thomas BOS 245
## 6 Kemba Walker CHO 240
## 7 Bradley Beal WAS 223
## 8 Damian Lillard POR 214
## 9 Ryan Anderson HOU 204
## 10 J.J. Redick LAC 201Create a data frame gsw_mpg of GSW players that contains variables for player name, experience and min_per_game sorted by min_per_game in descending order
gsw_mpg <- filter(dat, team == "GSW")
gsw_mpg <- mutate(gsw_mpg, min_per_game = minutes / games)
gsw_mpg <- select(gsw_mpg, player, experience, min_per_game)
gsw_mpg <- select(gsw_mpg, player, experience, min_per_game)
gsw_mpg <- arrange(gsw_mpg, desc(min_per_game))
gsw_mpg## # A tibble: 16 x 3
## player experience min_per_game
## <chr> <int> <dbl>
## 1 Klay Thompson 5 34.0
## 2 Stephen Curry 7 33.4
## 3 Kevin Durant 9 33.4
## 4 Draymond Green 4 32.5
## 5 Andre Iguodala 12 26.3
## 6 Matt Barnes 13 20.5
## 7 Zaza Pachulia 13 18.1
## 8 Shaun Livingston 11 17.7
## 9 Patrick McCaw 0 15.1
## 10 Ian Clark 3 14.8
## 11 David West 13 12.6
## 12 JaVale McGee 8 9.60
## 13 James Michael McAdoo 2 8.79
## 14 Damian Jones 0 8.5
## 15 Kevon Looney 1 8.43
## 16 Anderson Varejao 12 6.57Summarzing values with summarise()
summarise(dat, avg_salary = mean(salary))## # A tibble: 1 x 1
## avg_salary
## <dbl>
## 1 5804697.summarise(dat,
min = min(salary),
median = median(salary),
avg = mean(salary),
max = max(salary))## # A tibble: 1 x 4
## min median avg max
## <dbl> <dbl> <dbl> <dbl>
## 1 5145 3000000 5804697. 30963450Grouped operations
#average salary, grouped by team
summarise(
group_by(dat, team),
avg_salary = mean(salary)
)## # A tibble: 30 x 2
## team avg_salary
## <chr> <dbl>
## 1 ATL 5494447.
## 2 BOS 6127673.
## 3 BRK 4011351.
## 4 CHI 5781368.
## 5 CHO 5531548.
## 6 CLE 7069699.
## 7 DAL 5157128.
## 8 DEN 4648719.
## 9 DET 6871632.
## 10 GSW 6265160.
## # ... with 20 more rows#average salary, grouped by position
summarise(
group_by(dat, position),
avg_salary = mean(salary)
)## # A tibble: 5 x 2
## position avg_salary
## <chr> <dbl>
## 1 C 6529906.
## 2 PF 5801127.
## 3 PG 5601217.
## 4 SF 6042455.
## 5 SG 5114178.#average weight and height, grouped by position, in descending order of average height
arrange(
summarise(
group_by(dat, position),
avg_height = mean(height),
avg_weight = mean(weight)),
desc(avg_height)
)## # A tibble: 5 x 3
## position avg_height avg_weight
## <chr> <dbl> <dbl>
## 1 C 83.2 251.
## 2 PF 81.4 235.
## 3 SF 79.5 220.
## 4 SG 77.0 204.
## 5 PG 74.3 189.Use summarise() to get the largest height value
summarise(dat, max_height = max(height))## # A tibble: 1 x 1
## max_height
## <dbl>
## 1 87Use summarise() to get the standard deviation of points3
summarise(dat, sd_points3 = sd(points3))## # A tibble: 1 x 1
## sd_points3
## <dbl>
## 1 55.1Use summarise() and group_by() to display the median of three-points, by team
summarise(group_by(dat, team), median_points3 = median(points3))## # A tibble: 30 x 2
## team median_points3
## <chr> <dbl>
## 1 ATL 32
## 2 BOS 46
## 3 BRK 36
## 4 CHI 28.5
## 5 CHO 13
## 6 CLE 26.5
## 7 DAL 18
## 8 DEN 46
## 9 DET 28
## 10 GSW 10.5
## # ... with 20 more rowsDisplay the average triple points by team, in ascending order, of the bottom-5 teams
head(
arrange(
summarise(
group_by(dat, team),
avg_points3 = mean(points3)),
avg_points3),
5)## # A tibble: 5 x 2
## team avg_points3
## <chr> <dbl>
## 1 NOP 32.4
## 2 PHO 33.5
## 3 ORL 34.3
## 4 SAC 35.1
## 5 CHI 35.3Obtain the mean and standard deviation of age for Power Forwards, with 5 and 10 years(including) years of experience
pf_5_10 <- filter(dat, position == "PF" & (experience == 5 | experience == 10))
summarise(pf_5_10, mean_of_pf510 = mean(age), standard_deviation_pf510 = sd(age))## # A tibble: 1 x 2
## mean_of_pf510 standard_deviation_pf510
## <dbl> <dbl>
## 1 28 2.83First contact with ggplot()
# scatterplot (option 1)
ggplot(data = dat) + geom_point(aes(x = points, y = salary))
# scatterplot (option 2)
ggplot(data = dat, aes(x = points, y = salary)) + geom_point()
# colored scatterplot
ggplot(data = dat, aes(x = points, y = salary)) + geom_point(aes(color = position))
#sized and colored scatterplot
ggplot(data = dat, aes(x = points, y = salary)) + geom_point(aes(color = position, size = points3))
#sized and colored scatterplot but transparent
ggplot(data = dat, aes(x = points, y = salary)) +
geom_point(aes(color = position, size = points3), alpha = 0.7)
Use the data frame gsw to make a scatterplot of height and weight
ggplot(data = gsw, aes(x = height, y = weight)) + geom_point(aes(color = player), alpha = 0.4)
Find out how to make another scatterplot of height and weight, using geom_text() to display the names of the players
ggplot(data = gsw, aes(x = height, y = weight)) + geom_point(aes(color = player), alpha = 0.4) + geom_text(aes(label = player), nudge_x = 1, nudge_y = 1)
Get a scatterplot of height and weight, for ALL the warriors, displaying their names with geom_label()
gsw_all <- filter(dat, team == "GSW")
ggplot(data = gsw_all, aes(x = height, y = weight)) + geom_point(aes(color = player), alpha = 0.3) + geom_label(aes(label = player), nudge_x = 1, nudge_y = 1, alpha = 0.1)
Get a density plot of salary for all NBA players
ggplot(dat, aes(salary)) + geom_density(kernel = "gaussian", fill = "pink")
Get a histogram of points2 with binwidth of 50 for all NBA players
ggplot(dat, aes(points2)) + geom_histogram(binwidth = 50, fill = "lavender")
Get a barchart of the position frequencies for all NBA players
ggplot(dat, aes(position)) + geom_bar(fill = "light blue")
Make a scatterplot of experience and salary of all Centers and use geom_smooth() to add a regression line
centers <- filter(dat, position == "C")
ggplot(data = centers, aes(x = experience, y = salary)) + geom_point() + geom_smooth(method = lm)
Repeat the same scatterplot of experience and salary of all Centers, but now use geom_smooth() to add a loess line (i.e. smooth line)
ggplot(data = centers, aes(x = experience, y = salary)) + geom_point() + geom_smooth(method = loess)
Faceting
#scatterplot by position
ggplot(data = dat, aes(x = points, y = salary)) + geom_point() + facet_wrap(~ position)
# scatterplot by position
ggplot(data = dat, aes(x = points, y = salary)) +
geom_point(aes(color = position), alpha = 0.7) +
facet_grid(~ position) +
geom_smooth(method = loess)
# scatterplot by position
ggplot(data = dat, aes(x = points, y = salary)) +
geom_point(aes(color = position), alpha = 0.7) +
facet_grid(position ~ .) +
geom_smooth(method = loess)
Make scatterplots of experience and salary faceting by position
ggplot(data = dat, aes(x = experience, y = salary)) +
geom_point(aes(color = position), alpha = 0.7) +
facet_grid(position ~ .) +
geom_smooth(method = loess)
Make scatterplots of experience and salary faceting by team
ggplot(data = dat, aes(x = experience, y = salary)) + geom_point() + facet_wrap(team ~ .)
Make density plots of age faceting by team
ggplot(dat, aes(salary)) + geom_density(kernel = "gaussian", fill = "snow") + facet_wrap(team ~ .)
Make scatterplots of height and weight faceting by position
ggplot(data = dat, aes(x = height, y = weight)) + geom_point() + geom_point(aes(color = position), alpha = 0.7) + facet_wrap(position ~ .)
ggplot(data = dat, aes(x = height, y = weight)) + geom_point() + geom_point(aes(color = position), alpha = 0.7) + facet_grid(position ~ .)
Make scatterplots of height and weight, with a 2-dimensional density, geom_density2d(), faceting by position
ggplot(data = dat, aes(x = height, y = weight)) + geom_point() + geom_point(aes(color = position), alpha = 0.7) + facet_wrap(position ~ .) + geom_density2d()
ggplot(data = dat, aes(x = height, y = weight)) + geom_point() + geom_point(aes(color = position), alpha = 0.7) + facet_grid(position ~ .) + geom_density2d()
Make a scatterplot of experience and salary for the Warriors, but this time add a layer with theme_bw() to get a simpler background
warriors <- filter(dat, team == "GSW")
ggplot(data = warriors, aes(x = experience, y = salary)) + geom_point() + geom_point(aes(color = position), alpha = 0.7) + facet_grid(position ~ .) + theme_bw()
Repeat any of the previous plots but now adding a leyer with another theme e.g. theme_minimal(), theme_dark(), theme_classic()
ggplot(data = warriors, aes(x = experience, y = salary)) + geom_point() + geom_point(aes(color = position), alpha = 0.7) + facet_grid(position ~ .) + theme_minimal()
ggplot(data = warriors, aes(x = experience, y = salary)) + geom_point() + geom_point(aes(color = position), alpha = 0.7) + facet_grid(position ~ .) + theme_dark()
ggplot(data = warriors, aes(x = experience, y = salary)) + geom_point() + geom_point(aes(color = position), alpha = 0.7) + facet_grid(position ~ .) + theme_classic()
More shell commands
cd Desktop/stat133/demo-repo/labs/lab05/images
ls
ls -l
ls -lt
ls -r
mkdr ../copies
cp unnamed-chunk-32-1.png ../copies
cp *.png ../copies
cd ../copies
mv unnamed-chunk-32-1.png renamed-chunk-32-1.png
mv unnamed-chunk-32-2.png renamed-chunk-32-2.png
cd ../report
mv ../copies ../copy-files
rm ../copy-files/unnamed-chunk-32-3.png
rm ../copy-files/*.*
rmdir ../copy-files